




'''simple docstring'''



import argparse
import os.path as osp
import re

import torch
from safetensors.torch import load_file, save_file


# =================#
# UNet Conversion #
# =================#

lowerCamelCase_									=		[
    # (stable-diffusion, HF Diffusers)
    ("""time_embed.0.weight""", """time_embedding.linear_1.weight"""),
    ("""time_embed.0.bias""", """time_embedding.linear_1.bias"""),
    ("""time_embed.2.weight""", """time_embedding.linear_2.weight"""),
    ("""time_embed.2.bias""", """time_embedding.linear_2.bias"""),
    ("""input_blocks.0.0.weight""", """conv_in.weight"""),
    ("""input_blocks.0.0.bias""", """conv_in.bias"""),
    ("""out.0.weight""", """conv_norm_out.weight"""),
    ("""out.0.bias""", """conv_norm_out.bias"""),
    ("""out.2.weight""", """conv_out.weight"""),
    ("""out.2.bias""", """conv_out.bias"""),
]

lowerCamelCase_									=		[
    # (stable-diffusion, HF Diffusers)
    ("""in_layers.0""", """norm1"""),
    ("""in_layers.2""", """conv1"""),
    ("""out_layers.0""", """norm2"""),
    ("""out_layers.3""", """conv2"""),
    ("""emb_layers.1""", """time_emb_proj"""),
    ("""skip_connection""", """conv_shortcut"""),
]

lowerCamelCase_									=		[]
# hardcoded number of downblocks and resnets/attentions...
# would need smarter logic for other networks.
for i in range(4):
 # loop over downblocks/upblocks

 for j in range(2):
  # loop over resnets/attentions for downblocks
  lowerCamelCase_									=		f'''down_blocks.{i}.resnets.{j}.'''
  lowerCamelCase_									=		f'''input_blocks.{3*i + j + 1}.0.'''
  unet_conversion_map_layer.append((sd_down_res_prefix, hf_down_res_prefix))

  if i < 3:
   # no attention layers in down_blocks.3
   lowerCamelCase_									=		f'''down_blocks.{i}.attentions.{j}.'''
   lowerCamelCase_									=		f'''input_blocks.{3*i + j + 1}.1.'''
   unet_conversion_map_layer.append((sd_down_atn_prefix, hf_down_atn_prefix))

 for j in range(3):
  # loop over resnets/attentions for upblocks
  lowerCamelCase_									=		f'''up_blocks.{i}.resnets.{j}.'''
  lowerCamelCase_									=		f'''output_blocks.{3*i + j}.0.'''
  unet_conversion_map_layer.append((sd_up_res_prefix, hf_up_res_prefix))

  if i > 0:
   # no attention layers in up_blocks.0
   lowerCamelCase_									=		f'''up_blocks.{i}.attentions.{j}.'''
   lowerCamelCase_									=		f'''output_blocks.{3*i + j}.1.'''
   unet_conversion_map_layer.append((sd_up_atn_prefix, hf_up_atn_prefix))

 if i < 3:
  # no downsample in down_blocks.3
  lowerCamelCase_									=		f'''down_blocks.{i}.downsamplers.0.conv.'''
  lowerCamelCase_									=		f'''input_blocks.{3*(i+1)}.0.op.'''
  unet_conversion_map_layer.append((sd_downsample_prefix, hf_downsample_prefix))

  # no upsample in up_blocks.3
  lowerCamelCase_									=		f'''up_blocks.{i}.upsamplers.0.'''
  lowerCamelCase_									=		f'''output_blocks.{3*i + 2}.{1 if i == 0 else 2}.'''
  unet_conversion_map_layer.append((sd_upsample_prefix, hf_upsample_prefix))

lowerCamelCase_									=		"""mid_block.attentions.0."""
lowerCamelCase_									=		"""middle_block.1."""
unet_conversion_map_layer.append((sd_mid_atn_prefix, hf_mid_atn_prefix))

for j in range(2):
 lowerCamelCase_									=		f'''mid_block.resnets.{j}.'''
 lowerCamelCase_									=		f'''middle_block.{2*j}.'''
 unet_conversion_map_layer.append((sd_mid_res_prefix, hf_mid_res_prefix))
def 					SCREAMING_SNAKE_CASE_					(				__A							:					Tuple    )					->					Dict:
   # buyer beware: this is a *brittle* function,
   # and correct output requires that all of these pieces interact in
   # the exact order in which I have arranged them.
   _SCREAMING_SNAKE_CASE			= {k: k for k in unet_state_dict.keys()}
   for sd_name, hf_name in unet_conversion_map:
      _SCREAMING_SNAKE_CASE			= sd_name
   for k, v in mapping.items():
      if "resnets" in k:
         for sd_part, hf_part in unet_conversion_map_resnet:
            _SCREAMING_SNAKE_CASE			= v.replace(snake_case__			,	snake_case__    )
         _SCREAMING_SNAKE_CASE			= v
   for k, v in mapping.items():
      for sd_part, hf_part in unet_conversion_map_layer:
         _SCREAMING_SNAKE_CASE			= v.replace(snake_case__			,	snake_case__    )
      _SCREAMING_SNAKE_CASE			= v
   _SCREAMING_SNAKE_CASE			= {v: unet_state_dict[k] for k, v in mapping.items()}
   return new_state_dict


# ================#
# VAE Conversion #
# ================#

lowerCamelCase_									=		[
    # (stable-diffusion, HF Diffusers)
    ("""nin_shortcut""", """conv_shortcut"""),
    ("""norm_out""", """conv_norm_out"""),
    ("""mid.attn_1.""", """mid_block.attentions.0."""),
]

for i in range(4):
 # down_blocks have two resnets
 for j in range(2):
  lowerCamelCase_									=		f'''encoder.down_blocks.{i}.resnets.{j}.'''
  lowerCamelCase_									=		f'''encoder.down.{i}.block.{j}.'''
  vae_conversion_map.append((sd_down_prefix, hf_down_prefix))

 if i < 3:
  lowerCamelCase_									=		f'''down_blocks.{i}.downsamplers.0.'''
  lowerCamelCase_									=		f'''down.{i}.downsample.'''
  vae_conversion_map.append((sd_downsample_prefix, hf_downsample_prefix))

  lowerCamelCase_									=		f'''up_blocks.{i}.upsamplers.0.'''
  lowerCamelCase_									=		f'''up.{3-i}.upsample.'''
  vae_conversion_map.append((sd_upsample_prefix, hf_upsample_prefix))

 # up_blocks have three resnets
 # also, up blocks in hf are numbered in reverse from sd
 for j in range(3):
  lowerCamelCase_									=		f'''decoder.up_blocks.{i}.resnets.{j}.'''
  lowerCamelCase_									=		f'''decoder.up.{3-i}.block.{j}.'''
  vae_conversion_map.append((sd_up_prefix, hf_up_prefix))

# this part accounts for mid blocks in both the encoder and the decoder
for i in range(2):
 lowerCamelCase_									=		f'''mid_block.resnets.{i}.'''
 lowerCamelCase_									=		f'''mid.block_{i+1}.'''
 vae_conversion_map.append((sd_mid_res_prefix, hf_mid_res_prefix))


lowerCamelCase_									=		[
    # (stable-diffusion, HF Diffusers)
    ("""norm.""", """group_norm."""),
    ("""q.""", """query."""),
    ("""k.""", """key."""),
    ("""v.""", """value."""),
    ("""proj_out.""", """proj_attn."""),
]
def 					SCREAMING_SNAKE_CASE_					(				__A							:					int    )					->					Tuple:
   # convert HF linear weights to SD conv2d weights
   return w.reshape(*w.shape			,	1			,	1    )
def 					SCREAMING_SNAKE_CASE_					(				__A							:					Any    )					->					str:
   _SCREAMING_SNAKE_CASE			= {k: k for k in vae_state_dict.keys()}
   for k, v in mapping.items():
      for sd_part, hf_part in vae_conversion_map:
         _SCREAMING_SNAKE_CASE			= v.replace(snake_case__			,	snake_case__    )
      _SCREAMING_SNAKE_CASE			= v
   for k, v in mapping.items():
      if "attentions" in k:
         for sd_part, hf_part in vae_conversion_map_attn:
            _SCREAMING_SNAKE_CASE			= v.replace(snake_case__			,	snake_case__    )
         _SCREAMING_SNAKE_CASE			= v
   _SCREAMING_SNAKE_CASE			= {v: vae_state_dict[k] for k, v in mapping.items()}
   _SCREAMING_SNAKE_CASE			= ["q", "k", "v", "proj_out"]
   for k, v in new_state_dict.items():
      for weight_name in weights_to_convert:
         if f"""mid.attn_1.{weight_name}.weight""" in k:
            print(f"""Reshaping {k} for SD format"""    )
            _SCREAMING_SNAKE_CASE			= reshape_weight_for_sd(snake_case__    )
   return new_state_dict


# =========================#
# Text Encoder Conversion #
# =========================#


lowerCamelCase_									=		[
    # (stable-diffusion, HF Diffusers)
    ("""resblocks.""", """text_model.encoder.layers."""),
    ("""ln_1""", """layer_norm1"""),
    ("""ln_2""", """layer_norm2"""),
    (""".c_fc.""", """.fc1."""),
    (""".c_proj.""", """.fc2."""),
    (""".attn""", """.self_attn"""),
    ("""ln_final.""", """transformer.text_model.final_layer_norm."""),
    ("""token_embedding.weight""", """transformer.text_model.embeddings.token_embedding.weight"""),
    ("""positional_embedding""", """transformer.text_model.embeddings.position_embedding.weight"""),
]
lowerCamelCase_									=		{re.escape(x[1]): x[0] for x in textenc_conversion_lst}
lowerCamelCase_									=		re.compile('|'.join(protected.keys()))

# Ordering is from https://github.com/pytorch/pytorch/blob/master/test/cpp/api/modules.cpp
lowerCamelCase_									=		{"""q""": 0, """k""": 1, """v""": 2}
def 					SCREAMING_SNAKE_CASE_					(				__A							:					Dict    )					->					Any:
   _SCREAMING_SNAKE_CASE			= {}
   _SCREAMING_SNAKE_CASE			= {}
   _SCREAMING_SNAKE_CASE			= {}
   for k, v in text_enc_dict.items():
      if (
          k.endswith(".self_attn.q_proj.weight"    )
          or k.endswith(".self_attn.k_proj.weight"    )
          or k.endswith(".self_attn.v_proj.weight"    )
      ):
         _SCREAMING_SNAKE_CASE			= k[: -len(".q_proj.weight"    )]
         _SCREAMING_SNAKE_CASE			= k[-len("q_proj.weight"    )]
         if k_pre not in capture_qkv_weight:
            _SCREAMING_SNAKE_CASE			= [None, None, None]
         _SCREAMING_SNAKE_CASE			= v
         continue

      if (
          k.endswith(".self_attn.q_proj.bias"    )
          or k.endswith(".self_attn.k_proj.bias"    )
          or k.endswith(".self_attn.v_proj.bias"    )
      ):
         _SCREAMING_SNAKE_CASE			= k[: -len(".q_proj.bias"    )]
         _SCREAMING_SNAKE_CASE			= k[-len("q_proj.bias"    )]
         if k_pre not in capture_qkv_bias:
            _SCREAMING_SNAKE_CASE			= [None, None, None]
         _SCREAMING_SNAKE_CASE			= v
         continue

      _SCREAMING_SNAKE_CASE			= textenc_pattern.sub(lambda __A    : protected[re.escape(m.group(0    )    )]			,	snake_case__    )
      _SCREAMING_SNAKE_CASE			= v

   for k_pre, tensors in capture_qkv_weight.items():
      if None in tensors:
         raise Exception("CORRUPTED MODEL: one of the q-k-v values for the text encoder was missing"    )
      _SCREAMING_SNAKE_CASE			= textenc_pattern.sub(lambda __A    : protected[re.escape(m.group(0    )    )]			,	snake_case__    )
      _SCREAMING_SNAKE_CASE			= torch.cat(snake_case__    )

   for k_pre, tensors in capture_qkv_bias.items():
      if None in tensors:
         raise Exception("CORRUPTED MODEL: one of the q-k-v values for the text encoder was missing"    )
      _SCREAMING_SNAKE_CASE			= textenc_pattern.sub(lambda __A    : protected[re.escape(m.group(0    )    )]			,	snake_case__    )
      _SCREAMING_SNAKE_CASE			= torch.cat(snake_case__    )

   return new_state_dict
def 					SCREAMING_SNAKE_CASE_					(				__A							:					List[Any]    )					->					Optional[int]:
   return text_enc_dict


if __name__ == "__main__":
 lowerCamelCase_									=		argparse.ArgumentParser()

 parser.add_argument('--model_path', default=None, type=str, required=True, help='Path to the model to convert.')
 parser.add_argument('--checkpoint_path', default=None, type=str, required=True, help='Path to the output model.')
 parser.add_argument('--half', action='store_true', help='Save weights in half precision.')
 parser.add_argument(
     '--use_safetensors', action='store_true', help='Save weights use safetensors, default is ckpt.'
 )

 lowerCamelCase_									=		parser.parse_args()

 assert args.model_path is not None, "Must provide a model path!"

 assert args.checkpoint_path is not None, "Must provide a checkpoint path!"

 # Path for safetensors
 lowerCamelCase_									=		osp.join(args.model_path, 'unet', 'diffusion_pytorch_model.safetensors')
 lowerCamelCase_									=		osp.join(args.model_path, 'vae', 'diffusion_pytorch_model.safetensors')
 lowerCamelCase_									=		osp.join(args.model_path, 'text_encoder', 'model.safetensors')

 # Load models from safetensors if it exists, if it doesn't pytorch
 if osp.exists(unet_path):
  lowerCamelCase_									=		load_file(unet_path, device='cpu')
 else:
  lowerCamelCase_									=		osp.join(args.model_path, 'unet', 'diffusion_pytorch_model.bin')
  lowerCamelCase_									=		torch.load(unet_path, map_location='cpu')

 if osp.exists(vae_path):
  lowerCamelCase_									=		load_file(vae_path, device='cpu')
 else:
  lowerCamelCase_									=		osp.join(args.model_path, 'vae', 'diffusion_pytorch_model.bin')
  lowerCamelCase_									=		torch.load(vae_path, map_location='cpu')

 if osp.exists(text_enc_path):
  lowerCamelCase_									=		load_file(text_enc_path, device='cpu')
 else:
  lowerCamelCase_									=		osp.join(args.model_path, 'text_encoder', 'pytorch_model.bin')
  lowerCamelCase_									=		torch.load(text_enc_path, map_location='cpu')

 # Convert the UNet model
 lowerCamelCase_									=		convert_unet_state_dict(unet_state_dict)
 lowerCamelCase_									=		{"""model.diffusion_model.""" + k: v for k, v in unet_state_dict.items()}

 # Convert the VAE model
 lowerCamelCase_									=		convert_vae_state_dict(vae_state_dict)
 lowerCamelCase_									=		{"""first_stage_model.""" + k: v for k, v in vae_state_dict.items()}

 # Easiest way to identify v2.0 model seems to be that the text encoder (OpenCLIP) is deeper
 lowerCamelCase_									=		"""text_model.encoder.layers.22.layer_norm2.bias""" in text_enc_dict

 if is_vaa_model:
  # Need to add the tag 'transformer' in advance so we can knock it out from the final layer-norm
  lowerCamelCase_									=		{"""transformer.""" + k: v for k, v in text_enc_dict.items()}
  lowerCamelCase_									=		convert_text_enc_state_dict_vaa(text_enc_dict)
  lowerCamelCase_									=		{"""cond_stage_model.model.""" + k: v for k, v in text_enc_dict.items()}
 else:
  lowerCamelCase_									=		convert_text_enc_state_dict(text_enc_dict)
  lowerCamelCase_									=		{"""cond_stage_model.transformer.""" + k: v for k, v in text_enc_dict.items()}

 # Put together new checkpoint
 lowerCamelCase_									=		{**unet_state_dict, **vae_state_dict, **text_enc_dict}
 if args.half:
  lowerCamelCase_									=		{k: v.half() for k, v in state_dict.items()}

 if args.use_safetensors:
  save_file(state_dict, args.checkpoint_path)
 else:
  lowerCamelCase_									=		{"""state_dict""": state_dict}
  torch.save(state_dict, args.checkpoint_path)







"""simple docstring"""




def        a__					(							snake_case__ = 50_00_00_00							)		->   int:
			lowerCamelCase      =   set()
			lowerCamelCase      =   int((limit - 24) ** (1 / 2)							)

			lowerCamelCase      =   set(range(3			,					prime_square_limit + 1			,					2							)							)
			primes.add(2							)
			for p in range(3			,					prime_square_limit + 1			,					2							):
						if p not in primes:
									continue
						primes.difference_update(set(range(p * p			,					prime_square_limit + 1			,					snake_case__							)							)							)

			for primea in primes:
						lowerCamelCase      =   primea * primea
						for primea in primes:
									lowerCamelCase      =   primea * primea * primea
									if square + cube >= limit - 16:
												break
									for primea in primes:
												lowerCamelCase      =   primea * primea * primea * primea
												lowerCamelCase      =   square + cube + tetr
												if total >= limit:
															break
												ret.add(snake_case__							)

			return len(snake_case__							)


if __name__ == "__main__":
					print(F"""{solution() = }""")



